TTSurv aims to correlate coding and noncoding genes with cancers by combining high-throughput data with clinical prognosis. The application focuses on the use of high-throughput data to detect ncRNAs, such as lncRNAs and microRNAs, as novel diagnostic and prognostic biomarkers. For a more comprehensive analysis, a large amount of public expression profile data with clinical follow-up information. TTSurv also provides flexible methods such as a minimum p-value algorithm and unsupervised clustering methods that can classify thoracic cancer samples into different risk groups.
functional, regulatory and non-coding rna biomarkers pathology oncology gene expression data visualisation